Merge pull request #356 from SaigyoujiYusora/debug-fix-log
fix: 优化发送错误时图片大喷射
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SengokuCola
GitHub
@@ -185,9 +185,9 @@ class LLM_request:
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elif response.status in policy["abort_codes"]:
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logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
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if response.status == 403:
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#只针对硅基流动的V3和R1进行降级处理
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if self.model_name.startswith(
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"Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/":
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# 只针对硅基流动的V3和R1进行降级处理
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if self.model_name.startswith(
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"Pro/deepseek-ai") and self.base_url == "https://api.siliconflow.cn/v1/":
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old_model_name = self.model_name
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self.model_name = self.model_name[4:] # 移除"Pro/"前缀
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logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
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@@ -228,7 +228,7 @@ class LLM_request:
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try:
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chunk = json.loads(data_str)
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if flag_delta_content_finished:
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usage = chunk.get("usage", None) # 获取tokn用量
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usage = chunk.get("usage", None) # 获取tokn用量
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else:
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delta = chunk["choices"][0]["delta"]
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delta_content = delta.get("content")
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@@ -236,14 +236,14 @@ class LLM_request:
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delta_content = ""
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accumulated_content += delta_content
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# 检测流式输出文本是否结束
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finish_reason = chunk["choices"][0].get("finish_reason")
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finish_reason = chunk["choices"][0].get("finish_reason")
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if finish_reason == "stop":
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usage = chunk.get("usage", None)
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if usage:
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break
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# 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk
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flag_delta_content_finished = True
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except Exception:
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logger.exception("解析流式输出错误")
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content = accumulated_content
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@@ -254,7 +254,8 @@ class LLM_request:
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content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
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# 构造一个伪result以便调用自定义响应处理器或默认处理器
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result = {
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"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}], "usage": usage}
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"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}],
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"usage": usage}
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return response_handler(result) if response_handler else self._default_response_handler(
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result, user_id, request_type, endpoint)
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else:
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@@ -270,6 +271,9 @@ class LLM_request:
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await asyncio.sleep(wait_time)
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else:
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logger.critical(f"请求失败: {str(e)}")
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if image_base64:
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payload["messages"][0]["content"][1]["image_url"][
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"url"] = f"data:image/{image_format.lower()};base64,{image_base64[:10]}...{image_base64[-10:]}"
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logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}")
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raise RuntimeError(f"API请求失败: {str(e)}")
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@@ -307,7 +311,8 @@ class LLM_request:
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{"type": "image_url", "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}}
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{"type": "image_url",
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"image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"}}
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]
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}
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],
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@@ -452,6 +457,7 @@ class LLM_request:
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)
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return embedding
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def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
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"""压缩base64格式的图片到指定大小
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Args:
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@@ -463,36 +469,36 @@ def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 10
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try:
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# 将base64转换为字节数据
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image_data = base64.b64decode(base64_data)
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# 如果已经小于目标大小，直接返回原图
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if len(image_data) <= 2*1024*1024:
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if len(image_data) <= 2 * 1024 * 1024:
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return base64_data
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# 将字节数据转换为图片对象
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img = Image.open(io.BytesIO(image_data))
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# 获取原始尺寸
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original_width, original_height = img.size
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# 计算缩放比例
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scale = min(1.0, (target_size / len(image_data)) ** 0.5)
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# 计算新的尺寸
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new_width = int(original_width * scale)
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new_height = int(original_height * scale)
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# 创建内存缓冲区
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output_buffer = io.BytesIO()
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# 如果是GIF，处理所有帧
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if getattr(img, "is_animated", False):
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frames = []
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for frame_idx in range(img.n_frames):
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img.seek(frame_idx)
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new_frame = img.copy()
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new_frame = new_frame.resize((new_width//2, new_height//2), Image.Resampling.LANCZOS) # 动图折上折
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new_frame = new_frame.resize((new_width // 2, new_height // 2), Image.Resampling.LANCZOS) # 动图折上折
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frames.append(new_frame)
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# 保存到缓冲区
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frames[0].save(
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output_buffer,
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@@ -506,23 +512,22 @@ def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 10
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else:
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# 处理静态图片
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resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
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# 保存到缓冲区，保持原始格式
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if img.format == 'PNG' and img.mode in ('RGBA', 'LA'):
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resized_img.save(output_buffer, format='PNG', optimize=True)
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else:
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resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True)
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# 获取压缩后的数据并转换为base64
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compressed_data = output_buffer.getvalue()
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logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}")
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logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB")
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logger.info(f"压缩前大小: {len(image_data) / 1024:.1f}KB, 压缩后大小: {len(compressed_data) / 1024:.1f}KB")
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return base64.b64encode(compressed_data).decode('utf-8')
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except Exception as e:
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logger.error(f"压缩图片失败: {str(e)}")
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import traceback
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logger.error(traceback.format_exc())
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return base64_data
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return base64_data
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